What This Category’s Data Looks Like
Photovoltaic research report sources include public securities firm research report libraries, public documents from industry research institutions, and power equipment industry databases. Update frequency fluctuates with industry events and earnings report deadlines, with no fixed cycle. Most document structures include industrial chain data, policy clauses, and market forecast content. Fields include publishing entity, publish time, core indicator values, and industrial chain link names. Units use industry-standard physical units such as GW, yuan/watt, and ton. Single document length ranges from thousands to tens of thousands of characters.
What Constraints Do These Characteristics Impose on Vector Models and Indexing?
Photovoltaic research reports contain a large number of specialized terms and structured industry indicators. Vector models must have subfield semantic alignment capabilities. This prevents confusion between general terms and industry-specific expressions. Document updates fluctuate with industry events and earnings report deadlines. Indexes must support incremental synchronization without full reconstruction. This reduces update costs. Single document length varies widely. Adaptable segmentation strategies are required to preserve complete semantic units. Some documents contain cross-industry-chain related data. Cross-field vector association retrieval must be supported to improve content matching accuracy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | bge-m3 (local deployment) or text-embedding-v3 (cloud call) | Adapts to semantic encoding requirements for photovoltaic industry specialized terms, supports long text vector generation |
chunk_size | 800–1200 characters | Photovoltaic research reports contain industrial chain-related content. This segmentation length preserves complete semantics for single links and avoids semantic breaks across links |
recall_top_k | 10–15 results | Photovoltaic research report data density is high. This recall volume covers core related content while reducing redundant information |
rerank_top_k | 5–8 results | Filters low-similarity recall results, focuses on core related content, balances retrieval speed and accuracy |
similarity_threshold | 0.72–0.78 | Matches the professional content similarity distribution of photovoltaic research reports, filters irrelevant industry report content |
embedding_api_timeout | 30 seconds | Covers normal call durations for most cloud vector models, avoids index failure caused by timeouts |
The parameter values provided on this page are conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- A "vector access failed" prompt appears when calling a local vector model. Logs return connection timeout error code 504. The cause is incorrect configuration of Ollama's local port mapping, or FastGPT not adding the model's local access whitelist.
- After adding the
text-embedding-v3API key, the interface displays "no available channels". The cause is not enabling access permissions for the Alibaba Cloud vector model in the corresponding group, or the key not being bound to the correct resource group. - Knowledge base question and answer responses time out. The retrieval process takes more than 10 seconds. The cause is excessively high recall settings, and failure to adjust segmentation length to adapt to model encoding speed. This leads to excessive computational load for vector retrieval and reranking.
How to Confirm Successful Configuration
- Navigate to the FastGPT vector model management page. Check that the configured
embedding_modelstatus is "Connected" with no error prompts. - Upload a test photovoltaic research report. Check the parsed segmentation results to confirm the segmentation length falls within the preset
chunk_sizerange. - Initiate a research report retrieval test. Verify that the number of recalled entries and reranked returned entries match the configured values.
- View the vector index update logs. Confirm that incremental update tasks can be triggered normally with no failure records.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.